Metabob detects, explains, and fixes coding problems created by humans and AI
GNN detects and classifies problematic code with contextual understanding
Problematic code along with enriched context is stored in Metabob's backend
The stored information from the backend is passed to an integrated LLM
The LLM generates a context-sensitive problem explanation and resolution
Metabob's AI is trained on millions of bug fixes performed by experienced developers. The ability to understand code logic and context, enables Metabob to detect complex problems that span across codebases and automatically generates fixes for them.
Metabob detects hundreds of logical problems, varying from race conditions to unhandled edge cases. Such problems cannot be detected with traditional static analysis tools (e.g., Sonarqube, Deepsource).
Integrate to analyze each pull request to improve code quality, reliability and software security by fixing problems before you merge. No CI setup required.
Before merging code, Metabob ensures that known security vulnerabilities are detected to stay compliant with software security industry standards. In addition, Metabob's AI is able to detect complex security vulnerabilities that require contextual and logical understanding of the code base.
SANS/CWE top 25, OWASP top 10, MITRE CWE.
Present top features:
- Minimal false positive rate (< 5%)
- Security gate integration
- Secrets scanning
Debug faster by automatically generated code fix recommendations and enforce code quality and best practices with Metabob’s refactoring recommendations.
Metabob’s ability to analyze complete code bases allows it to generate context-sensitive code recommendations for found bugs and code smells.
Metabob enforces code quality and best practices by offering refactoring recommendations for areas with messy and ineffective code, ultimately reducing the creation of technical debt and optimizing LOC performance.
Metabob can be deployed on-premise on your organization’s private cloud and customized to detect problems that are the most relevant to your team.
such as Sonarqube and linters
After analyzing the whole codebase, Metabob uses generative AI to facilitate code review and improve software security
Intermittent server crashes
App unable to start new threads after run period
Data is overrepresented in certain batches
App using 100% available CPU on certain setups
Instead of generating code based on prompts, Metabob analyzes and fixes existing code
Don't take our word for it, take theirs.